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pcalg (version 2.7-5)

Methods for Graphical Models and Causal Inference

Description

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

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Version

Install

install.packages('pcalg')

Monthly Downloads

1,575

Version

2.7-5

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

February 22nd, 2022

Functions in pcalg (2.7-5)

addBgKnowledge

Add background knowledge to a CPDAG or PDAG
adjustment

Compute adjustment sets for covariate adjustment.
GaussL0penIntScore-class

Class "GaussL0penIntScore"
ParDAG-class

Class "ParDAG" of Parametric Causal Models
GaussL0penObsScore-class

Class "GaussL0penObsScore"
Score-class

Virtual Class "Score"
ages

Estimate an APDAG within the Markov equivalence class of a DAG using AGES
GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models
binCItest

G square Test for (Conditional) Independence of Binary Variables
LINGAM

Linear non-Gaussian Acyclic Models (LiNGAM)
EssGraph-class

Class "EssGraph"
dag2cpdag

Convert a DAG to a CPDAG
beta.special

Compute set of intervention effects
corGraph

Computing the correlation graph
dsep

Test for d-separation in a DAG
checkTriple

Check Consistency of Conditional Independence for a Triple of Nodes
beta.special.pcObj

Compute set of intervention effects in a fast way
dsepAM

Test for d-separation in a MAG
amatType

Types and Display of Adjacency Matrices in Package 'pcalg'
dag2essgraph

Convert a DAG to an Essential Graph
backdoor

Find Set Satisfying the Generalized Backdoor Criterion (GBC)
dag2pag

Convert a DAG with latent variables into a PAG
gies

Estimate Interventional Markov Equivalence Class of a DAG by GIES
iplotPC

Plotting a pcAlgo object using the package igraph
idaFast

Multiset of Possible Total Causal Effects for Several Target Var.s
opt.target

Get an optimal intervention target
compareGraphs

Compare two graphs in terms of TPR, FPR and TDR
mcor

Compute (Large) Correlation Matrix
getNextSet

Iteration through a list of all combinations of choose(n,k)
getGraph

Get the "graph" Part or Aspect of R Object
condIndFisherZ

Test Conditional Independence of Gaussians via Fisher's Z
fci

Estimate a PAG with the FCI Algorithm
fciAlgo-class

Class "fciAlgo" of FCI Algorithm Results
dsepAMTest

Test for d-separation in a MAG
disCItest

G square Test for (Conditional) Independence of Discrete Variables
dreach

Compute D-SEP(x,y,G)
gmB

Graphical Model 5-Dim Binary Example Data
gmG

Graphical Model 8-Dimensional Gaussian Example Data
gmD

Graphical Model Discrete 5-Dim Example Data
optAdjSet

Compute the optimal adjustment set
pag2anc

Reads off identifiable ancestors and non-ancestors from a directed PAG
pag2mag

Transform a PAG into a MAG in the Corresponding Markov Equivalence Class
pc

Estimate the Equivalence Class of a DAG using the PC Algorithm
possibleDe

[DEPRECATED] Find possible descendants on definite status paths.
pcalg-internal

Internal Pcalg Functions
possDe

Find possible descendants of given node(s).
showEdgeList

Show Edge List of pcAlgo object
pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection
fciPlus

Estimate a PAG with the FCI+ Algorithm
find.unsh.triple

Find all Unshielded Triples in an Undirected Graph
simy

Estimate Interventional Markov Equivalence Class of a DAG
ida

Estimate Multiset of Possible Joint Total Causal Effects
legal.path

Check if a 3-node-path is Legal
gmL

Latent Variable 4-Dim Graphical Model Data Example
pcAlgo

PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG
mat2targets

Conversion between an intervention matrix and a list of intervention targets
gAlgo-class

Class "gAlgo"
pcSelect

PC-Select: Estimate subgraph around a response variable
pc.cons.intern

Utility for conservative and majority rule in PC and FCI
isValidGraph

Check for a DAG, CPDAG or a maximally oriented PDAG
randomDAG

Generate a Directed Acyclic Graph (DAG) randomly
randDAG

Random DAG Generation
pcalg2dagitty

Transform the adjacency matrix from pcalg into a dagitty object
gac

Test If Set Satisfies Generalized Adjustment Criterion (GAC)
dsepTest

Test for d-separation in a DAG
gds

Greedy DAG Search to Estimate Markov Equivalence Class of DAG
jointIda

Estimate Multiset of Possible Total Joint Effects
pdag2allDags

Enumerate All DAGs in a Markov Equivalence Class
pag2conf

Reads off identifiable unconfounded node pairs from a directed PAG
trueCov

Covariance matrix of a DAG.
pag2edge

Reads off identifiable parents and non-parents from a directed PAG
skeleton

Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm
ges

Estimate the Markov equivalence class of a DAG using GES
pcAlgo-class

Class "pcAlgo" of PC Algorithm Results, incl. Skeleton
pdag2dag

Extend a Partially Directed Acyclic Graph (PDAG) to a DAG
gmInt

Graphical Model 8-Dimensional Interventional Gaussian Example Data
pcorOrder

Compute Partial Correlations
gmI

Graphical Model 7-dim IDA Data Examples
qreach

Compute Possible-D-SEP(x,G) of a node x in a PDAG G
rmvnorm.ivent

Simulate from a Gaussian Causal Model
plotSG

Plot the subgraph around a Specific Node in a Graph Object
searchAM

Search for certain nodes in a DAG/CPDAG/MAG/PAG
pdsep

Estimate Final Skeleton in the FCI algorithm
showAmat

Show Adjacency Matrix of pcAlgo object
shd

Compute Structural Hamming Distance (SHD)
udag2apag

Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG
plotAG

Plot partial ancestral graphs (PAG)
wgtMatrix

Weight Matrix of a Graph, e.g., a simulated DAG
possAn

Find possible ancestors of given node(s).
udag2pag

Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG
r.gauss.pardag

Generate a Gaussian Causal Model Randomly
rfci

Estimate an RFCI-PAG using the RFCI Algorithm
rmvDAG

Generate Multivariate Data according to a DAG
udag2pdag

Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG
visibleEdge

Check visible edge.